Update app.py

app.py

@@ -138,7 +138,7 @@ demo = gr.Interface(
 
 demo.launch()
 '''
-import gradio as gr
+'''import gradio as gr
 from transformers import TFBertForSequenceClassification, BertTokenizer
 import tensorflow as tf
 import praw
@@ -213,5 +213,91 @@ demo = gr.Interface(
 )
             
 demo.launch()
+'''
+import gradio as gr
+from transformers import TFBertForSequenceClassification, BertTokenizer, pipeline
+import tensorflow as tf
+import praw
+import os
+
+# Load main BERT model and tokenizer
+model = TFBertForSequenceClassification.from_pretrained("shrish191/sentiment-bert")
+tokenizer = BertTokenizer.from_pretrained("shrish191/sentiment-bert")
+
+# Load fallback sentiment pipeline model
+fallback_classifier = pipeline("text-classification", model="VinMir/GordonAI-sentiment_analysis")
+
+# Label mapping for main model
+LABELS = {
+    0: "Neutral",
+    1: "Positive",
+    2: "Negative"
+}
+
+# Reddit API setup (secure credentials from Hugging Face secrets)
+reddit = praw.Reddit(
+    client_id=os.getenv("REDDIT_CLIENT_ID"),
+    client_secret=os.getenv("REDDIT_CLIENT_SECRET"),
+    user_agent=os.getenv("REDDIT_USER_AGENT", "sentiment-classifier-script")
+)
+
+# Fetch content from Reddit URL
+def fetch_reddit_text(reddit_url):
+    try:
+        submission = reddit.submission(url=reddit_url)
+        return f"{submission.title}\n\n{submission.selftext}"
+    except Exception as e:
+        return f"Error fetching Reddit post: {str(e)}"
+
+# Sentiment classification function
+def classify_sentiment(text_input, reddit_url):
+    if reddit_url.strip():
+        text = fetch_reddit_text(reddit_url)
+    elif text_input.strip():
+        text = text_input
+    else:
+        return "[!] Please enter some text or a Reddit post URL."
+
+    if text.lower().startswith("error") or "Unable to extract" in text:
+        return f"[!] {text}"
+
+    try:
+        # Main BERT model prediction
+        inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True)
+        outputs = model(inputs)
+        probs = tf.nn.softmax(outputs.logits, axis=1)
+        confidence = float(tf.reduce_max(probs).numpy())
+        pred_label = tf.argmax(probs, axis=1).numpy()[0]
+
+        if confidence < 0.5:
+            # Use fallback model silently
+            fallback = fallback_classifier(text)[0]['label']
+            return f"Prediction: {fallback}"
+
+        return f"Prediction: {LABELS[pred_label]}"
+    except Exception as e:
+        return f"[!] Prediction error: {str(e)}"
+
+# Gradio interface
+demo = gr.Interface(
+    fn=classify_sentiment,
+    inputs=[
+        gr.Textbox(
+            label="Text Input (can be tweet or any content)",
+            placeholder="Paste tweet or type any content here...",
+            lines=4
+        ),
+        gr.Textbox(
+            label="Reddit Post URL",
+            placeholder="Paste a Reddit post URL (optional)",
+            lines=1
+        ),
+    ],
+    outputs="text",
+    title="Sentiment Analyzer",
+    description="🔍 Paste any text (including tweet content) OR a Reddit post URL to analyze sentiment.\n\n💡 Tweet URLs are not supported directly due to platform restrictions. Please paste tweet content manually."
+)
+
+demo.launch()